Maximum-Likelihood Expectation-Maximization Algorithm Versus Windowed Filtered Backprojection Algorithm: A Case Study.
Filtered backprojection (FBP) algorithms reduce image noise by smoothing the image. Iterative algorithms reduce image noise by noise weighting and regularization. It is believed that iterative algorithms are able to reduce noise without sacrificing image resolution, and thus iterative algorithms, es...
| Publicado en: | Journal of Nuclear Medicine Technology Vol. 46; no. 2; pp. 129 - 133 |
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| Autor principal: | |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Society of Nuclear Medicine
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135100141&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135100141 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00914916 H7Q jtl: Journal of Nuclear Medicine Technology issn: 00914916 maglogo: N pubinfo: dt: Jun2018 vid: 46 iid: 2 pid: 2576 pub: Society of Nuclear Medicine place: Reston, Virginia artinfo: ui: 135100141 135100141 NLM29438005 135100141 10.2967/jnmt.117.196311 NLM29438005 135100141 ppf: 129 ppct: 4 formats: fmt: @attributes: type: P tig: atl: Maximum-Likelihood Expectation-Maximization Algorithm Versus Windowed Filtered Backprojection Algorithm: A Case Study. aug: au: Zeng, Gengsheng L. affil: Department of Engineering, Weber State University, Ogden, Utah sug: subj: Image Processing, Computer Assisted Methods Algorithms Sensitivity and Specificity Computer Simulation Phantoms, Imaging Probability Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Filtered backprojection (FBP) algorithms reduce image noise by smoothing the image. Iterative algorithms reduce image noise by noise weighting and regularization. It is believed that iterative algorithms are able to reduce noise without sacrificing image resolution, and thus iterative algorithms, especially maximum-likelihood expectation maximization (MLEM), are used in nuclear medicine to replace FBP algorithms. Methods: This short paper uses counter examples to show that this belief is not true. We compare image noise variance for FBP and MLEM reconstructions having the same spatial resolution. Results: The truth is that although MLEM suppresses image noise, it does so by sacrificing image resolution as well; the performance of windowed FBP may be better than that of MLEM in our case study. Conclusion: The myth of the superiority of iterative algorithms is caused by comparing them with conventional FBP instead of with windowed FBP. However, we do not intend to generalize the comparison results to imply which algorithm is more favorable. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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